A central premise in modern cancer treatment is that patient diagnosis, prognosis, risk assessment, and treatment response prediction can be improved by stratification of cancers based on genomic, transcriptional and epigenomic characteristics of the tumor alongside relevant clinical information gathered at the time of diagnosis (for example, patient history, tumor histology and stage) as well as subsequent clinical follow-up data (for example, treatment regimens and disease recurrence events).
With the release of multiple tumor and matched normal whole genome sequences from projects like The Cancer Genome Atlas (TCGA), there is great need for computationally efficient tools that can extract as much genomic information as possible from these enormous datasets (TCGA, 2008). Considering that a single patient's whole genome sequence at high coverage (>30×) can be hundreds of gigabytes in compressed form, an analysis comparing a pair of these large datasets is slow and difficult to manage, but absolutely necessary in order to discover the many genomic changes that occurred in each patient's tumor.
Breast cancer is clinically and genomically heterogeneous and is composed of several pathologically and molecularly distinct subtypes. Patient responses to conventional and targeted therapeutics differ among subtypes motivating the development of marker guided therapeutic strategies. Collections of breast cancer cell lines mirror many of the molecular subtypes and pathways found in tumors, suggesting that treatment of cell lines with candidate therapeutic compounds can guide identification of associations between molecular subtypes, pathways and drug response. In a test of 77 therapeutic compounds, nearly all drugs show differential responses across these cell lines and approximately half show subtype-, pathway and/or genomic aberration-specific responses. These observations suggest mechanisms of response and resistance that may inform clinical drug deployment as well as efforts to combine drugs effectively.
There is currently a need to provide methods that can be used in characterization, diagnosis, treatment, and determining outcome of diseases and disorders.